Faculty profile
Dominique Poirel
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Research
Latest papers
Semi-analytical hierarchical Bayesian inference of nonlinear model structure in stochastic dynamics: Applied to compartmental models of infectious diseases.
PloS one · 2026
A Bayesian model calibration framework for stochastic compartmental models with both time-varying and time-invariant parameters.
Infectious Disease Modelling · 2024
Comprehensive compartmental model and calibration algorithm for the study of clinical implications of the population-level spread of COVID-19: a study protocol.
BMJ open · 2022
Latest funding
- $22,000
Fundamental aeroelastic studies in non-idealized conditions
NSERC · 2024 · Principal investigator
- $250,000
A long-term predictive modeling and uncertainty quantification framework for covid-19 using novel machine learning algorithms
SSHRC · 2022 · Co-investigator
- $192,000
Nonlinear Aeroelasticity and Unsteady Aerodynamics
NSERC · 2018 · Principal investigator
3 publications.
Semi-analytical hierarchical Bayesian inference of nonlinear model structure in stochastic dynamics: Applied to compartmental models of infectious diseases.
Robinson B, Bisaillon P, Sandhu R, Khalil M, Edwards JD, Kendzerska T, Walker T, Mills S, Pettit C, Poirel D, Sarkar A
A Bayesian model calibration framework for stochastic compartmental models with both time-varying and time-invariant parameters.
Robinson B, Bisaillon P, Edwards JD, Kendzerska T, Khalil M, Poirel D, Sarkar A
Comprehensive compartmental model and calibration algorithm for the study of clinical implications of the population-level spread of COVID-19: a study protocol.
Robinson B, Edwards JD, Kendzerska T, Pettit CL, Poirel D, Daly JM, Ammi M, Khalil M, Taillon PJ, Sandhu R, Mills S, Mulpuru S, Walker T, Percival V, Dolean V, Sarkar A
Fundamental aeroelastic studies in non-idealized conditions
Principal investigators: Poirel, Dominique
A long-term predictive modeling and uncertainty quantification framework for covid-19 using novel machine learning algorithms
Principal investigators: Sarkar, Abhijit
Keywords: covid-19; machine learning; high performance computing; Bayesian inference; pandemic modeling
Nonlinear Aeroelasticity and Unsteady Aerodynamics
Principal investigators: Poirel, Dominique
Keywords: aeroelasticity; energy harvesting; flutter; large deformations; limit cycle oscillations; low Reynolds numbers; nonlinear dynamics; transonic aerodynamics; uncertainty quantification; unsteady aerodynamics
Nonlinear Aeroelasticity and Unsteady Aerodynamics
Principal investigators: Poirel, Dominique
Aeroelasticity and unsteady aerodynamics at low reynolds numbers
Principal investigators: Poirel, Dominique
Nonlinear aeroelasticity and unsteady aerodynamics
Principal investigators: Poirel, Dominique
Non-deterministic treatment of aeroelastic dynamics
Principal investigators: Poirel, Dominique
From CIHR, NSERC and SSHRC funding decisions: CIHR since 2008, NSERC since 1991 and SSHRC since 1998, including their latest published competition results.
Frequent collaborators
- Sunita Mulpuru and Tetyana Kendzerska: 12 shared papers
- Tetyana Kendzerska and Abhijit Sarkar: 4 shared papers
- Tetyana Kendzerska and Dominique Poirel: 3 shared papers
- Dominique Poirel and Abhijit Sarkar: 3 shared papers
- Sunita Mulpuru and Mehdi Ammi: 1 shared paper
- Sunita Mulpuru and Dominique Poirel: 1 shared paper
- Sunita Mulpuru and Abhijit Sarkar: 1 shared paper
- Mehdi Ammi and Tetyana Kendzerska: 1 shared paper
- Mehdi Ammi and Dominique Poirel: 1 shared paper
- Department of Medicine
- Department of Mechanical and Aerospace Engineering
- Civil and Environmental Engineering
- Public Policy
Co-authors at Royal Military College of Canada, colored by department. Thicker lines mean more shared papers; select anyone to open their profile and their own map.
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